Submission¶

Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace

In [ ]:
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [ ]:
#load data
df = px.data.gapminder()
df.head()
Out[ ]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4

Question 1:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [ ]:
# extracting the data from 2007
df_2007 = df[
    (df['year'] == 2007)
]   

# Labels of the continents
continents_2007 = df_2007.groupby('continent',as_index=False)

# Create a bar chart using Plotly Express
fig = px.bar(continents_2007.sum(), x="pop", y="continent", orientation='h',color="continent", text="pop")

fig.update_layout(showlegend=False)

# Using the layout of the example plot
fig.update_layout(yaxis = {'categoryorder':'category descending'})
fig.update_layout(yaxis = dict(title = 'continent'))
fig.update_layout(xaxis = dict(title = 'pop'))
In [ ]:
# YOUR CODE HERE

Question 2:¶

Sort the order of the continent for the visualisation

Hint: Use axis layout setting

In [ ]:
# YOUR CODE HERE
fig.update_layout(yaxis = {'categoryorder':'total ascending'})

Question 3:¶

Add text to each bar that represents the population

In [ ]:
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
In [ ]:
# YOUR CODE HERE

Question 4:¶

Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years

In [ ]:
# Filtering of the dataset
df_continents = df.groupby(['continent','year'], as_index=False)
df_continents.sum()

# Plot 
fig = px.bar(df_continents.sum(),x="pop", y="continent", orientation='h',
             color="continent", animation_frame="year", animation_group="continent",
             range_x=[0, 4000000000])

fig.update_layout(showlegend = False)
fig.update_layout(yaxis = {'categoryorder':'total ascending'})

fig.show()
In [ ]:
# YOUR CODE HERE

Question 5:¶

Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years

In [ ]:
fig = px.bar(df,x="pop", y="country", orientation='h',
             color="country", animation_frame="year",range_x = [0, 1500000000])

fig.update_layout(showlegend = False)
fig.update_layout(yaxis = {'categoryorder':'total ascending'})
fig.show()
In [ ]:
# YOUR CODE HERE

Question 6:¶

Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation

In [ ]:
fig.update_layout(height = 1000)
In [ ]:
# YOUR CODE HERE

Question 7:¶

Show only the top 10 countries in the animation

Hint: Use the axis limit to set this.

In [ ]:
range_countries = len(df['country'].unique())
fig.update_yaxes(range=[range_countries-10.5, range_countries-0.5])
fig.update_layout(height = None)
In [ ]:
# YOUR CODE HERE